Toward Safer Digital Communication: A Deep Hybrid Model for Detecting Abusive Language on Social Networks
Received: 13 June 2025 | Revised: 19 July 2025 and 27 July 2025 | Accepted: 1 August 2025 | Online: 12 August 2025
Corresponding author: Aigerim Toktarova
Abstract
The prevalence of abusive language and cyberbullying on social media platforms presents a growing challenge to the user safety and digital well-being, necessitating the development of effective automated content moderation systems. This study proposes a hybrid deep learning model that combines Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNNs) to classify the abusive text with enhanced accuracy and contextual awareness. The LSTM component captures long-range dependencies and semantic context, while the CNN module extracts discriminative local n-gram features. The model was trained and evaluated on three benchmark datasets: HatebaseTwitter, HatEval, and TRAC. The experimental results demonstrated that the proposed architecture outperforms traditional classifiers, such as SVM, Random Forest, and Logistic Regression, as well as standalone CNN and LSTM models, achieving superior performance across all standard evaluation metrics. Notably, the model attained AUC scores of up to 0.97, indicating robust discriminatory power. These findings underscore the effectiveness of the hybrid LSTM–CNN model for abusive language detection and highlight its potential for deployment in real-time content moderation tools aimed at fostering safer online communication environments.
Keywords:
abusive language detection, cyberbullying classification, deep learning, LSTM-CNN hybrid model, text classification, social media analysis, natural language processing, sentiment analysisDownloads
References
M. P. Akhter, Z. Jiangbin, I. R. Naqvi, M. AbdelMajeed, and T. Zia, "Abusive language detection from social media comments using conventional machine learning and deep learning approaches," Multimedia Systems, vol. 28, no. 6, pp. 1925–1940, Dec. 2022.
P. K. Roy and A. Kumar, "Ensuring safety in digital spaces: Detecting code-mixed hate speech in social media posts," Data & Knowledge Engineering, vol. 156, Mar. 2025, Art. no. 102409.
R. Prabhu and V. Seethalakshmi, "A comprehensive framework for multi-modal hate speech detection in social media using deep learning," Scientific Reports, vol. 15, no. 1, Apr. 2025, Art. no. 13020.
H. Han, M. Asif, E. M. Awwad, N. Sarhan, Y. Y. Ghadi, and B. Xu, "Innovative deep learning techniques for monitoring aggressive behavior in social media posts," Journal of Cloud Computing, vol. 13, no. 1, p. 19, Jan. 2024.
R. Kumar and A. Bhat, "A study of machine learning-based models for detection, control, and mitigation of cyberbullying in online social media," International Journal of Information Security, vol. 21, no. 6, pp. 1409–1431, Dec. 2022.
A. T. Azar, H. M. Noori, A. R. Mahlous, A. Al-Khayyat, and I. K. Ibraheem, "Quasi-Reflection Learning Arithmetic Firefly Search Optimization with Deep Learning-based Cyberbullying Detection on Social Networking," Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 17162–17169, Oct. 2024.
S. Narynov, D. Mukhtarkhanuly, and B. Omarov, "Dataset of depressive posts in Russian language collected from social media," Data in Brief, vol. 29, Apr. 2020, Art. no. 105195.
M. Neog and N. Baruah, "A hybrid deep learning approach for Assamese toxic comment detection in social media," Procedia Computer Science, vol. 235, pp. 2297–2306, Jan. 2024.
N. A. Samee, U. Khan, S. Khan, M. M. Jamjoom, M. Sharif, and D. H. Kim, "Safeguarding Online Spaces: A Powerful Fusion of Federated Learning, Word Embeddings, and Emotional Features for Cyberbullying Detection," IEEE Access, vol. 11, pp. 124524–124541, 2023.
S. Abarna, J. I. Sheeba, and S. Pradeep Devaneyan, "A novel ensemble model for identification and classification of cyber harassment on social media platform," Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 13–36, Jul. 2023.
B. Omarov, Z. Zhumanov, A. Gumar, and L. Kuntunova, "Artificial Intelligence Enabled Mobile Chatbot Psychologist using AIML and Cognitive Behavioral Therapy," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 6, 2023.
A. Rawat, S. Kumar, and S. S. Samant, "Hate speech detection in social media: Techniques, recent trends, and future challenges," WIREs Computational Statistics, vol. 16, no. 2, 2024, Art. no. e1648.
D. Sultan et al., "A Review of Machine Learning Techniques in Cyberbullying Detection," Computers, Materials & Continua, vol. 74, no. 3, pp. 5625–5640, 2022.
Md. A. Rahman, S. M. N. Sadat, A. T. Asyhari, N. Refat, M. N. Kabir, and R. A. Arshah, "A Secure and Sustainable Framework to Mitigate Hazardous Activities in Online Social Networks," IEEE Transactions on Sustainable Computing, vol. 6, no. 1, pp. 30–42, Jan. 2021.
S. Unnava and S. R. Parasana, "A Study of Cyberbullying Detection and Classification Techniques: A Machine Learning Approach," Engineering, Technology & Applied Science Research, vol. 14, no. 4, pp. 15607–15613, Aug. 2024.
R. Singh et al., "Deep Learning for Multi-Class Antisocial Behavior Identification From Twitter," IEEE Access, vol. 8, pp. 194027–194044, 2020.
F. N. Al-Wesabi et al., "Automatic Recognition of Cyberbullying in the Web of Things and social media using Deep Learning Framework," IEEE Transactions on Big Data, vol. 11, no. 1, pp. 259–270, Oct. 2025.
V. U. Gongane, M. V. Munot, and A. D. Anuse, "Detection and moderation of detrimental content on social media platforms: current status and future directions," Social Network Analysis and Mining, vol. 12, no. 1, Sep. 2022, Art. no. 129.
K. K. Mohbey, B. Agarwal, N. Kesswani, M. Sterjanov, Y. Nikol, and V. Margarita, "Hate Speech Identification and Categorization on Social Media Using Bi-LSTM: An Information Science Perspective," Journal of Information Science Theory and Practice, vol. 13, no. 1, pp. 51–69, 2025.
"Hatebase," HATEBASE.
V. Basile et al., "SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter," in Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota, USA, Mar. 2019, pp. 54–63.
"TRAC-2024," kaggle.
R. T. Potla, "AI-Powered Threat Detection in Online Communities: A Multi-Modal Deep Learning Approach," Journal of Computer and Communications, vol. 13, no. 2, pp. 155–171, Feb. 2025, https://doi.org/10.4236/jcc.2025.132010.
G. Ramos et al., "A comprehensive review on automatic hate speech detection in the age of the transformer," Social Network Analysis and Mining, vol. 14, no. 1, Oct. 2024, Art. no. 204.
A. Khan, A. Ahmed, S. Jan, M. Bilal, and M. F. Zuhairi, "Abusive Language Detection in Urdu Text: Leveraging Deep Learning and Attention Mechanism," IEEE Access, vol. 12, pp. 37418–37431, 2024.
K. Mnassri et al., "A survey on multi-lingual offensive language detection," PeerJ Computer Science, vol. 10, Mar. 2024, Art. no. e1934.
M. Raj, S. Singh, K. Solanki, and R. Selvanambi, "An Application to Detect Cyberbullying Using Machine Learning and Deep Learning Techniques," SN Computer Science, vol. 3, no. 5, Jul. 2022, Art. no. 401.
N. Khanduja, N. Kumar, and A. Chauhan, "Telugu language hate speech detection using deep learning transformer models: Corpus generation and evaluation," Systems and Soft Computing, vol. 6, Dec. 2024, Art. no. 200112.
G. Ramos et al., "Leveraging Transfer Learning for Hate Speech Detection in Portuguese Social Media Posts," IEEE Access, vol. 12, pp. 101374–101389, 2024.
E. N. Crothers, N. Japkowicz, and H. L. Viktor, "Machine-Generated Text: A Comprehensive Survey of Threat Models and Detection Methods," IEEE Access, vol. 11, pp. 70977–71002, 2023.
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Copyright (c) 2025 Akbayan Aliyeva, Balnur Kenjayeva, Moldir Kizdarbekova, Bolganay Kaldarova, Satmyrza Mamikov, Bauyrzhan Omarov, Nurlan Omarov, Aigerim Toktarova, Eshref Adaly
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